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IntroductionThe grain supply-demand balance is a long-standing concern for many countries and is essential to guaranteeing social stability, maintaining economic development and ensuring national grain security.MethodsBased on the data of 65 countries along the “Belt and Road” (B&R) from 1993 to 2021, this paper analyzed the spatiotemporal evolution, the matching relationship, regional grain security situation and driving factors of grain supply-demand by measuring and classifying the grain self-sufficiency rate using methods such as geostatistical analysis and the GTWR model.ResultsThe results indicated the following: (1) The B&R region was still at the primary stage of “food-based and feed-supplemented”. Grain supply and demand in the B&R region showed a steady upward trend, with grain yield contributing more to grain supply than sown area. (2) Overall, the B&R region has been largely self-sufficient since 2007, with grain supply meeting demand, but the level of grain self-sufficiency varied considerably between countries. (3) More than 58% of the countries were in grain insecurity, concentrated in West Asia-Middle East and South-East Asia. The gravity center of both grain supply and demand was near East Asia. (4) In terms of matching supply-demand, most countries fell into the category of high supply-high demand and low supply-low demand, with basically the same level of grain supply and demand. (5) Grain yield had the largest positive impact on grain supply-demand, GDP had the largest negative impact, and temperature change and precipitation change had a relatively small effect. The effects of fertilizer use, grain yield, and GDP on grain supply-demand fluctuated greatly over time.DiscussionThese findings can provide a scientific basis for the country to formulate policies for a sustainable grain supply-demand system.

Optical molecular tomography (OMT) is a potential pre-clinical molecular imaging technique with applications in a variety of biomedical areas, which can provide non-invasive quantitative three-dimensional (3D) information regarding tumor distribution in living animals. The construction of optical transmission models and the application of reconstruction algorithms in traditional model-based reconstruction processes have affected the reconstruction results, resulting in problems such as low accuracy, poor robustness, and long-time consumption. Here, a gates joint locally connected network (GLCN) method is proposed by establishing the mapping relationship between the inside source distribution and the photon density on surface directly, thus avoiding the extra time consumption caused by iteration and the reconstruction errors caused by model inaccuracy. Moreover, gates module was composed of the concatenation and multiplication operators of three different gates. It was embedded into the network aiming at remembering input surface photon density over a period and allowing the network to capture neurons connected to the true source selectively by controlling three different gates. To evaluate the performance of the proposed method, numerical simulations were conducted, whose results demonstrated good performance in terms of reconstruction positioning accuracy and robustness.

The COVID-19 pandemic is a major public health emergency that has caused significant global devastation. However, it has also fostered unprecedented worldwide solidarity. During this crisis, we have witnessed large-scale donations and assistance both domestically and internationally. In the face of such extensive public engagement, understanding the driving factors behind public donations is crucial in responding to future global shocks like the COVID-19 pandemic. This study proposes an analytical framework and examines the factors influencing public donation intention during major public health emergencies and their interactions. Based on the online and telephone survey data of 11,682 responses collected in China during the COVID-19 pandemic in 2020, this study employs multiple logistic regression and moderation effect models to analyze these influencing factors and their interactions on public donation intention. The findings reveal a remarkably high level of public engagement, with 79% of respondents expressing donation intention. Further analysis indicates that the hardship level caused by the pandemic, degree of risk perception, community material support, and evaluation of the pandemic response all have a significant and positive impact on public donation intention. Moreover, the evaluation of the pandemic response and community material support significantly and positively moderate the impact of the hardship level caused by the pandemic and degree of risk perception on public donation intention, respectively. This study provides valuable guidance for governments and organizations worldwide. It is helpful for enriching crisis management theory and improving crisis response mechanisms.

BackgroundIn traditional Chinese medicine, Jintiange capsules are frequently used to treat metabolic bone diseases and strengthen bones and tendons. The main component of Jintiange capsules is bionic tiger bone powder. However, the active ingredients and proteins are derived from other animal bones, with chemical profiles similar to that of natural tiger bone. This study aimed to explore the efficacy of Jintiange capsules, a Chinese herbal medicine, in the postoperative treatment of osteoporotic vertebral compression fractures (OVCFs).MethodsIn this systematic review, literature was retrieved using PubMed, the Cochrane Library, the Chinese National Knowledge Infrastructure, the Web of Science, the Wanfang Database, the Chinese Biomedical Literature Database, and the Chinese VIP Database from inception to July 2023. The primary outcome measures were the bone mineral density (BMD) and effective rate. The secondary outcome measures were the visual analog pain score (VAS), Oswestry disability index (ODI), Cobb’s angle, serum osteocalcin, serum alkaline phosphatase, and adverse events. RevMan 5.4 and STATA 17.0 software were used for data analysis.ResultsWe enrolled randomized controlled trials (RCTs) focusing on 1,642 patients in the meta-analysis. The meta-analysis illustrated that Jintiange capsules significantly increased the BMD of the lumbar spine (p

Abstract Rain can severely hamper the visibility of scene objects. Although existing deep learning methods have reported promising performance, they often fail to obtain satisfactory results in many practical situations, especially when the input image contains both rain streaks and haze‐like degradation. In this paper, a new two‐stage method based on attention smoothed dilated network (SDN) is proposed. Unlike most fully‐supervised methods, the mixture of rain streaks and haze‐like effects is considered in the model. The proposed method consists of two stages. First, a generative adversarial network guided by the rain‐streak attention map is proposed to remove rain streaks, where a multi‐stage attention module is used to accurately locate rain streaks in the generator. Second, haze‐like effects are further removed through SDN with the same structure as the generator. Extensive experiments on multiple datasets show that the method outperforms the state‐of‐the‐art in both objective evaluation and visual quality.

With the development of ultrafast science, free-electron lasers (FELs) with ultrashort pulses have become a state-of-the-art tool in ultrafast phenomena studies. In an externally seeded FEL, the output pulse duration is usually determined both by the seed laser pulse duration and FEL amplification process, which can hardly reach the timescale of a few femtoseconds. In this study, through a simple method of changing the relative time delay and correspondingly the pulse energy of the two seed lasers employed in a seeded FEL, we demonstrated the possibility of generating few-femtosecond soft X-ray pulses and controlling the final FEL pulse durations. Based on theoretical calculations and practical experiments, we conducted a detailed study on the capabilities and limitations to this method with the parameters of the Shanghai Soft X-ray FEL Facility. Start-to-end simulations indicate that we can achieve ultrashort soft X-ray FEL pulses with the pulse duration down to 5.2 fs, and the final pulse durations can also be controlled in terms of relative time delays.

Abstract Deraining has been attracting a lot of attention from researchers, and various methods have been proposed, especially deep‐networks are widely adopted in recent years. Their structures and learning become more and more complicated and diverse, making it difficult to analyze the contributions and improvements. In this paper, a comprehensive review for current rain removal methods is first provided to show their contributions. Specifically, they are reviewed in terms of handing rain streaks and rain mist. Second, besides evaluating their rain removal ability, they are also evaluated in terms of their impact on subsequent stereo‐matching task. To this end, a new deraining dataset is first prepared, called Rain‐Kitti2012 and Rain‐Kitti2015. They are created by adding rain part to clean image‐pairs in Kitti2012 and Kitti2015. By then, nine state‐of‐the‐art deraining methods are evaluated with full‐reference and no‐reference image quality assessment metrics. Furthermore, the blurriness and distortion types introduced during deraining are measured. Finally, three learning‐based stereo matching methods are compared, and they take the outputs of deraining methods as inputs. It is further discussed how derained images influence the accuracy of stereo matching, which can provide some insight for jointly handling rain removal and stereo matching. 1: A comprehensive review for the current rain removal methods is provided. They are categorized into rain‐streak‐oriented and rain‐mist‐oriented approaches in terms of degradation type, and are categorized into model‐driven and data‐driven approaches in terms of methodology. 2: A new image deraining dataset is introduced, which is the first dataset that can be used to perform stereo‐matching‐driven evaluation for deraining methods. The dataset is created by adding rain part to clean images in KITTI2012 and KITTI2015. 3: We evaluate 9 deep learning based deraining methods with full‐reference and no‐ reference metrics. In addition, the types of distortions produced by these methods are discussed and measured quantitatively. And, the impact of 9 deraining methods on the subsequent stereo matching task is evaluated, which can provide some insight on how to design stereo matching task‐driven deraining methods.

Abstract Salient target detection in hyperspectral image is a significant task in image segmentation, target tracking, image classification and so on. Many existing saliency detection algorithms for hyperspectral image detection cannot present the boundary of the salient target well and the description of the target is not enough. A method based on visual attention to detect the salient target of hyperspectral image is proposed in this paper. In this method, frequency‐tuned (FT) salient detection model is combined with spectral salient to detect target in hyperspectral image. FT model is used to get target with clear border, and spectral information is made full use of to improve the accuracy of target detection. Firstly, FT is used to detect saliency of hyperspectral image and the saliency map is generated. Then, spectral information of the hyperspectral image is measured by similarity, and the spectral saliency is obtained by calculating spectral angle distance between the spectral vectors. Finally, the FT's saliency map and the spectral saliency map are combined to form the final saliency target maps. Experimental results show that our method is superior to other methods in saliency target detection of hyperspectral image, and the precision‐recall curve and F‐measure are better as well.

Abstract Single image de‐raining based on convolutional neural network (CNN) has made considerable progress in recent years. However, usually the de‐rained result has dark artifacts and image textures tend to be over‐smoothed. In this paper, a pyramid non‐local enhanced residual dense network is proposed to reduce such distortion. Firstly, the down‐sampled images are input into the Laplacian pyramid, which can extract the overall and partial texture clues, and subsequently a set of images of different scales are produced. Secondly, these images are fed into a non‐local enhanced residual dense block, which can not only capture long‐distance dependencies of feature maps, but also fully utilizes the hierarchical features in every dense block, leading to high accuracy of rain streaks extraction and better preservation of image edge detail. Finally, the de‐rained image is gradually restored by Gaussian reconstruction pyramid. Experimental results on both synthetic data and real‐world data show that the artifacts distortion is obviously reduced by the proposed network. And the quality of de‐rained image is significantly improved compared with the state‐of‐the‐art methods.

Limited by the existing imagery sensors, a hyperspectral image (HSI) is characterized by its high spectral resolution but low spatial resolution. HSI super-resolution (SR) aims to enhance the spatial resolution of the HSIs without modifying the equipment and has become a hot issue for HSI processing. In this paper, inspired by two important observations, a gated content-oriented residual dense network (GCoRDN) is designed for the HSI SR. To be specific, based on the observation that the structure and texture exhibit different sensitivities to the spatial degradation, a content-oriented network with two branches is designed. Meanwhile, a weight-sharing strategy is merged in the network to preserve the consistency in the structure and the texture. In addition, based on the observation of the super-resolved results, a gating mechanism is applied as a form of post-processing to further enhance the SR performance. Experimental results and data analysis on both ground-based HSIs and airborne HSIs have demonstrated the effectiveness of the proposed method.

Limited by the imagery sensors, hyperspectral image (HSI) is characterized by its high spectral resolution, but low spatial resolution. HSI superresolution (SR) aims at enhancing the spatial resolution through the postprocessing techniques. Inspired by the important observation that the structure and the texture exhibit different sensitivities to the spatial resolution, we propose an HSI SR method via a residual structure-texture dense network. Specifically, there are three modules that are designed in the SR process. First, the input HSI is decomposed into the texture cube and the structural cube via relative total variation. Second, the mapping between the texture-structure cube of the input HSI and that of the desired HSI is learnt via two parallel subnetworks. Finally, the deep learnt texture cube is interfused with the deep learnt structural cube to achieve the reconstructed HSI with high spatial resolution via a upsample module. Contrary to the existed methods that mainly focus on the entire scene, our approach opens a new way for superresolving the HSI from a structure and texture recovery aspect. Experimental results and data analysis on both indoor scenes and outdoor scenarios have demonstrated the effectiveness of the proposed method.

BackgroundModified Duhuo Jisheng Decoction (MDHJSD) is a traditional Chinese medicine prescription for the treatment of osteoporosis (OP), but its mechanism of action has not yet been clarified. This study aims to explore the mechanism of MDHJSD in OP through a combination of network pharmacology analysis and experimental verification.MethodsThe active ingredients and corresponding targets of MDHJSD were acquired from the Traditional Chinese Medicine System Pharmacology (TCMSP) database. OP-related targets were acquired from databases, including Genecards, OMIM, Drugbank, CTD, and PGKB. The key compounds, core targets, major biological processes, and signaling pathways of MDHJSD that improve OP were identified by constructing and analysing the relevant networks. The binding affinities between key compounds and core targets were verified using AutoDock Vina software. A rat model of ovariectomized OP was used for the experimental verification.ResultsA total of 100 chemical constituents, 277 targets, and 4734 OP-related targets of MDHJSD were obtained. Subsequently, five core components and eight core targets were identified in the analysis. Pathway enrichment analysis revealed that overlapping targets were significantly enriched in the tumour necrosis factor-alpha (TNF-α) signaling pathway, an inflammation signaling pathway, which contained six of the eight core targets, including TNF-α, interleukin 6 (IL-6), transcription factor AP-1, mitogen-activated protein kinase 3, RAC-alpha serine/threonine-protein kinase, and caspase-3 (CASP3). Molecular docking analysis revealed close binding of the six core targets of the TNF signaling pathway to the core components. The results of experimental study show that MDHJSD can protect bone loss, inhibit the inflammatory response, and downregulate the expression levels of TNF-α, IL-6, and CASP3 in ovariectomized rats.ConclusionThe mechanism of MDHJSD in the treatment of OP may be related to the regulation of the inflammatory response in the bone tissue.

Taking Shandong Province as the research area, we explored the willingness of farmers to grow grain and the influencing factors. By constructing the evaluation system of their willingness with 6 levels and 15 indicators based on field investigation, and quantitatively analyzing the influence degree and impact assessment of factors through a logistic regression model, the regional differences in farmers’ willingness were summarized, and the influencing factors were recognized. This study indicates that there were obvious regional differences in farmers’ willingness, which were the highest in the western region, the second in the eastern region, and the lowest in the central region. Specifically, the willingness varies significantly among cities, among which Laiwu has the highest willingness (0.76), while Tai’an has the lowest (0.41). The level of urbanization in different regions and the main influencing factors are different, and the same factor has different degrees of influence on cities, leading to regional differences. In terms of urbanization level, the main influencing factors in areas with high urbanization levels are the proportion of grain income and grain expenditure. However, in areas with a low urbanization level, it is the farmers’ planting attitude. From the perspective of influence mode, different factors have positive and negative differences in the willingness. Additionally, farmers’ willingness is becoming more and more rational, and more consideration is given to economic benefits. Among the influencing factors, the land planting mode, the proportion of grain income, and the proportion of grain expenditure are the most important factors, and 82% (11) of the cities are affected by the above three factors. Finally, the corresponding incentive measures are proposed by the regional differences in the influencing factors in various cities.

This study aims to investigate the one-way cyclic lateral responses of piles in sloping ground by means of experimental and theoretical analyses. For this purpose, a series of laboratory model experiments were performed for different cases of cycle number, load amplitude, and slope angle, and lateral static loading tests on both level ground and sloping ground were conducted for comparison. Based on these experimental observations, the effects of cycle number, load amplitude, and slope angle on the pile head deflection and the profiles of bending moment and subgrade reaction are discussed. The pile deflection profile is difficult to measure directly owing to the restriction of experimental conditions, and thus a supplementary finite beam element method (FBEM) is provided to compensate for this deficiency. The comparisons between experimental and theoretical results demonstrate that the FBEM can well predict the pile responses in sloping ground, either under one-way cyclic lateral loading or lateral static loading.

The settlement calculation model for soft clay foundation is established based on Hooke’s law and the Duncan-Chang model. By introducing the concept of damage ratio, the method for determining the deformation modulus of soil before and after damage under load is presented, and a nonlinear settlement calculation method which considers the structural characteristics of soft clay is proposed. In the end, a practical engineering example is analyzed with the proposed method and some current methods for soft clay settlement calculation. The results indicated that the proposed method is feasible and applicable in practical engineering.

The settlement calculation of composite foundation bidirectionally reinforced by piles and geosynthetics is always a difficult problem. The key to its accuracy lies in the determination of pile-soil stress ratio. Based on the theory of double parameters of the elastic foundation plate, the horizontal geosynthetics of composite foundation are regarded as the elastic thin plate, and the vertical piles and surrounding soil are regarded as a series of springs with different stiffness. The deflection equation of horizontal geosynthetics considering its bending and pulling action is obtained according to the static equilibrium conditions. The equation is solved by using Bessel function of complex variable, and the corresponding deflection function of horizontal geosynthetics is deduced. Then, the calculation formula of pile-soil stress ratio and settlement of composite foundation is derived by considering the deformation coordination of pile and soil. The results of engineering case analysis show that the theoretical calculation results are in good agreement with the measured values, which indicates that the proposed method is feasible and the calculation accuracy is good. Finally, the influence of composite modulus of horizontal geosynthetics, tensile force of geosynthetics, and pile-soil stiffness ratio on pile-soil stress ratio and settlement is further analyzed. The results show that the pile-soil stress ratio increases with the increase of the composite modulus of the horizontal geosynthetics, the tensile force of geosynthetics, and the pile-soil stiffness ratio, and the settlement decreases with the increase of the composite modulus of the horizontal geosynthetics, the tensile force of geosynthetics, and the pile-soil stiffness ratio. When the flexural stiffness of the horizontal geosynthetics is small, the influence of the tensile action of the geosynthetics on the pile-soil stress ratio and settlement of the composite foundation cannot be ignored.

The analysis of the bearing characteristics and deformation mechanism of composite foundation reinforced with geogrid-encased stone columns is presented in order to obtain its settlement calculation method. The settlement of composite foundation is divided into three sections which are the reinforced section, unreinforced section, and underlying stratum. Based on Hooke’s law of space problem and the thoughts of the layer-wise summation method, the relative slip displacement between pile and soil of reinforced section without plastic zone is analyzed. The settlement of reinforced section is calculated by the layered iteration method based on the pile element model. The compatibility of vertical and radial deformations of unreinforced section is analyzed based on the pile-soil element model. The settlement of underlying stratum is still calculated by the layer-wise summation method. Finally, two engineering examples are analyzed and the results show that the settlement calculated by the presented method is close to the measured one. The method overcomes the defect that the calculated results by the other existing methods are more dangerous and it is more feasible and can be applied in engineering practice.

In soft soil foundations, geogrid encased stone column composite foundation technology has been widely applied and developed in recent years due to its efficient treatment. In this study, eight groups of laboratory model tests were performed in a large-scale testing tank to investigate the bearing mechanism and stress characteristics of the composite foundation of geogrid encased stone columns under traffic loads with different cyclic load ratios. The stress at the bottom of the stone column and settlement of the composite foundation were measured and analysed. The test results show that cyclic shearing will cause the rearrangement of the soil particles at the column-soil interface, which will cause changes in the face pressure and effective stress state of the column-soil boundary. The cyclic load has a substantial influence on the accumulation settlement of the composite foundation and the development of the lateral stress state of the column. Based on the test results, the development law of the cumulative settlement is summarized, and the change mechanism of the column stress state is analysed and discussed.

We present a saliency-guided algorithm for detecting the locations of repetitive structures on building facades. First, the global and local saliencies of each point are determined by measuring the global rarity and the local distinctness. The saliency map is utilized to adaptively extract the salient points. Second, the salient points are vertically sliced. A curve can be derived by counting the total number of points in each slice. Then, the curve is converted into a square wave to locate the vertical splitting position. Next, each segment is horizontally sliced, similar to the vertical splitting. The salient points are partitioned into repetitive candidates after the vertical and horizontal splitting. Finally, the repetitive candidates are refined according to the similarity of the neighborhood and the regularity of the arrangement. The experimental results demonstrate that our method can quickly and effectively extract repetitions from facade point clouds.

The images that are captured in sand storms often suffer from low contrast and serious color cast that are caused by sand dust, and these issues will have significant negative effects on the performance of an outdoor computer vision system. To address these problems, a method based on halo-reduced dark channel prior (DCP) dehazing for sand dust image enhancement is proposed in this paper. It includes three components in sequence: color correction in the LAB color space based on gray world theory, dust removal using a halo-reduced DCP dehazing method, and contrast stretching in the LAB color space using a Gamma function improved contrast limited adaptive histogram equalization (CLAHE), in which a guided filter is used to improve the artifacts of the histogram equalization. Experiments on a large number of real sand dust images demonstrate that the proposed method can well remove the overall yellowing tone and dust haze effect and obtain normal visual colors and a detailed clear image.

To deal with the serious visual artifacts caused by the consistent restoration algorithm on local motion blurred images, a novel two stage blur region detection method including coarse location and region refinement for local blurred images is proposed in this paper. First, blur feature discriminant criteria in frequency domain and spatial domain is defined to generate blur feature image; then, binarization is used on the blur feature image to obtain the coarse blur region detection map; subsequently, morphological methods are used to process the clear and blur areas and the trimap is obtained. Finally, the refinement detection of blur region is achieved by combination of the trimap and automatic KNN matting algorithm. Experimental results show that the proposed algorithm can detect the local blurred region quickly and effectively, and it has outperformed other traditional methods in the application of blur region detection and image restoration as well.

Abstract Because there is no temporal information available, rain removal with a single image is more challenging than that with a video. In this paper, we present a weighted median guided filtering method for rain removal with a single image. It consists of two filtering operations. Firstly, a weighted median filter is convoluted with an input rainy image to obtain a coarse rain-free image; then, guided filter is employed to obtain a refined rain-free image, where the coarse rain-free image is used as a guided image and convoluted with the input rainy image via guided filter. Experimental results show that the proposed method generated comparable results to the state-of-the-art algorithms with low computation cost.

Abstract Nighttime low illumination image enhancement is highly desired for outdoor computer vision applications. However, few works have been studied towards this goal. In addition, the low illumination enhancement problem becomes very challenging when the depth information of a low illumination image is unknown. To address this problem, in this paper, we propose a dual channel prior-based method for nighttime low illumination image enhancement with a single image, which builds upon two existing image priors: dark channel prior and bright channel prior. We utilize the bright channel prior to get an initial transmission estimate and then use the dark channel as a complementary channel to correct potentially erroneous transmission estimates attained from the bright channel prior. Experimental results show significant credibility of the approach both visually and by quantitative comparison with existing methods.

To improve the efficiency of blur kernel estimation based on prior knowledge, a method of deblurring an image based on rich edge region extraction using a gray-level co-occurrence matrix is proposed in this paper. First, the relationship between the image edge information and the related coefficients of a gray-level co-occurrence matrix is analyzed, based on which an index representing the amount of image edge information is proposed. Next, high-frequency layer information is extracted from the blurred image to be processed with a bilinear interpolation method in the luminance channel. Subsequently, the high-frequency layer image is divided into nine regions, based on a sliding window, and the rich edge region index of each region is calculated; then, the region with the richest edge information is extracted. Finally, the extracted rich edge region, instead of the entire motion blurred image, is used to estimate the blur kernel with L0-regularized intensity and gradient prior, and the blurred image is blindly restored. An image quality evaluation function and the operation time are used to evaluate the performance of the proposed method. Experimental results show that the proposed method can improve the recovery efficiency while ensuring the recovery quality as well.

Sketch-drawing is one of the simplest and most direct means to illustrate 3-D objects. It can not only present geometric features, but also greatly facilitate us to identify and understand the object. This paper presents a simple yet effective tool to generate view-dependent sketch-drawing from point clouds. First, we extract the ridge-valley lines in geometric regions by utilizing their curvature; meanwhile, we extract the contours of point clouds based on the object view-dependent; the obtained ridge-valley lines and contours are optimized and fused to describe the geometric features of the object. Second, we apply the change models of line thickness diffusion based on angle and depth to finish the line thickness variation. Finally, the shadow region of sketch-drawing is achieved by using the shadow generation model based on line density and gray value. Experimental results show that our proposed method can effectively complete the object sketch-drawing simulation from point clouds. Furthermore, our method is more flexible and robust than the existing algorithms; it does not require the preprocessing on the input point clouds.

Three-dimensional scanning technology is becoming increasingly popular in the Internet of Things and cyber-physical social systems. Existing reconstruction algorithms and shape extraction algorithms usually require regularized points and clear topological relationships between them. In this paper, a novel slicing-based method is proposed to regularize raw point clouds, in which we first slice the raw point to get cross section points, and then generate regular point clouds under the topology control of cross section shapes, and finally produce regular point clouds at multiple levels of detail. The proposed method is effective specifically for huge raw point clouds and the experiment results show that the proposed method can produce regularized point clouds for surface meshing, shapes analysis, shapes extraction, and so on.

Hyperspectral image (HSI) super-resolution has gained great attention in remote sensing, due to its effectiveness in enhancing the spatial information of the HSI while preserving the high spectral discriminative ability, without modifying the imagery hardware. In this paper, we proposed a novel HSI super-resolution method via a gradient-guided residual dense network (G-RDN), in which the spatial gradient is exploited to guide the super-resolution process. Specifically, there are three modules in the super-resolving process. Firstly, the spatial mapping between the low-resolution HSI and the desired high-resolution HSI is learned via a residual dense network. The residual dense network is used to fully exploit the hierarchical features learned from all the convolutional layers. Meanwhile, the gradient detail is extracted via a residual network (ResNet), which is further utilized to guide the super-resolution process. Finally, an empirical weight is set between the fully obtained global hierarchical features and the gradient details. Experimental results and the data analysis on three benchmark datasets with different scaling factors demonstrated that our proposed G-RDN achieved favorable performance.

By employing adaptive finite element limit analysis (AFELA), the seismic bearing capacity of strip footing on cohesive soil slopes are investigated. To consider the earthquake effects, the pseudostatic method is used. The upper and lower bounds for the seismic bearing capacity factor (Nce) are calculated, and the relative errors between them are found within 3% or better by adopting the adaptive mesh strategy. Based on the obtained results, design tables and charts are provided to facilitate engineers use, and the effects of footing position, undrained shear strength, slope angle, slope height, and pseudostatic acceleration coefficient are studied in detail. The collapse mechanisms are also discussed, including overall slope failure and foundation failure.

This paper, with a focuses on the pile-supported embankment with a conical pile-head, proposes a theoretical solution which incorporates all the load transfer mechanisms, namely the soil arching effect, the pile−soil interaction, and the support from the substratum, whilst an improved cylindrical unit cell model is introduced to analyze the soil arching effect. The theoretical solution has been verified via numerical analysis and a literature method. The comparative results indicate that the proposed theoretical solution can effectively evaluate the pile-supported embankment with a conical pile-head. Furthermore, parametric studies have also been conducted to analyze the effect of model parameters on the load sharing ratio (ne), the pile−soil stress ratio (n), and the pile shaft friction.

Limited by the existing imagery sensors, hyperspectral images are characterized by high spectral resolution but low spatial resolution. The super-resolution (SR) technique aiming at enhancing the spatial resolution of the input image is a hot topic in computer vision. In this paper, we present a hyperspectral image (HSI) SR method based on a deep information distillation network (IDN) and an intra-fusion operation. Specifically, bands are firstly selected by a certain distance and super-resolved by an IDN. The IDN employs distillation blocks to gradually extract abundant and efficient features for reconstructing the selected bands. Second, the unselected bands are obtained via spectral correlation, yielding a coarse high-resolution (HR) HSI. Finally, the spectral-interpolated coarse HR HSI is intra-fused with the input HSI to achieve a finer HR HSI, making further use of the spatial-spectral information these unselected bands convey. Different from most existing fusion-based HSI SR methods, the proposed intra-fusion operation does not require any auxiliary co-registered image as the input, which makes this method more practical. Moreover, contrary to most single-based HSI SR methods whose performance decreases significantly as the image quality gets worse, the proposal deeply utilizes the spatial-spectral information and the mapping knowledge provided by the IDN, which achieves more robust performance. Experimental data and comparative analysis have demonstrated the effectiveness of this method.

The lipid droplet (LD) is an organelle enveloped by a monolayer phospholipid membrane with a core of neutral lipids, which is conserved from bacteria to humans. The available evidence suggests that the LD is essential to maintaining lipid homeostasis in almost all organisms. As a consequence, LDs also play an important role in pathological metabolic processes involving the ectopic storage of neutral lipids, including type 2 diabetes mellitus (T2DM), atherosclerosis, steatosis, and obesity. The degree of insulin resistance in T2DM patients is positively correlated with the size of skeletal muscle LDs. Aerobic exercise can reduce the occurrence and development of various metabolic diseases. However, trained athletes accumulate lipids in their skeletal muscle, and LD size in their muscle tissue is positively correlated with insulin sensitivity. This phenomenon is called the athlete’s paradox. This review will summarize previous studies on the relationship between LDs in skeletal muscle and metabolic diseases and will discuss the paradox at the level of LDs.

Location-based services for mobile users provide a convenient means of filtering information based on current geographical position. However, the information in traditional location service systems is provided through an abstract metaphor that the user has to understand and translate into action. In this paper, a mobile augmented reality-based system for individual location recommendation is proposed. The system consists of the modules of user location, marker detection, three-dimensional (3D) display and location guide by combing augmented reality with navigation technology. Through such a system, the position and orientation information from any sensory source were pervasively extracted and used to enhance the association to the real world by combining video techniques and 3D-graphics in an augmented reality view. Experimental results demonstrate that the proposed system is both efficient and effective in helping people locate their position. Compared with the traditional Global Positioning Systems-based location services system, the proposed system can also enhance users’ senses experience by providing users with a more intuitive, 3D, dynamic information display and sharing capabilities.